From 6fad5d0a200402ce29daa7f76b2f18e49b47e45c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Christian=20H=C3=B6fert?=
<christian.hoefert@reutlingen-university.de>
Date: Thu, 18 Nov 2021 09:36:31 +0000
Subject: [PATCH] Upload New File
---
crh_LinearRegression.py | 74 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 74 insertions(+)
create mode 100644 crh_LinearRegression.py
diff --git a/crh_LinearRegression.py b/crh_LinearRegression.py
new file mode 100644
index 0000000..400348c
--- /dev/null
+++ b/crh_LinearRegression.py
@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 17 20:17:16 2021
+
+Provides functions for calculating the coeffs (m,c) of the
+linear regression y=mx+c for a given set of point-data [x,y]
+
+flag: plot
+
+@author: hoefert
+"""
+
+## https://www.mathematik.de/algebra/74-erste-hilfe/algebra/matrizen/2432-anwendungen-der-matrizenrechnung
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def linearRegression(x, y, plot = False):
+ # creating the LES A'A ( m c)' = A'y
+ # with A = (x , 1)
+
+ # creating LES (over determined)
+ n = len(x)
+ A= np.ones((n,2))
+ A[:,0] = np.transpose(x)
+ b = np.transpose(y)
+
+ M = np.matmul(A.T,A)
+
+ bTrans = np.matmul(A.T,b)
+ [m,c] = np.linalg.solve(M,bTrans)
+
+ if plot:
+ plotLinearRegression(x, y, m, c)
+
+ return m,c
+
+
+def logLinearRegression(x, y, plot = False):
+
+ xLog = np.log10(x)
+ yLog = np.log10(y)
+
+ m,c = linearRegression(xLog,yLog)
+
+ if plot:
+ plotLog10LinearRegression(x, y, m, c)
+
+ return m,c
+
+
+def plotLinearRegression(x,y,m,c):
+ xLin = np.linspace(min(x),max(x),100)
+ yLin = xLin*m+c
+ plt.plot(x,y,'o')
+ plt.plot(xLin,yLin,linewidth=2)
+ plt.grid('both')
+ plt.show()
+
+
+def plotLog10LinearRegression(x,y,m,c, plotLog = False):
+ plt.plot(x,y,'o')
+ xLin = np.linspace(min(x),max(x),100)
+ yLin = 10**c*xLin**m
+ plt.plot(xLin,yLin,linewidth=2)
+ if plotLog:
+ plt.xscale('log')
+ plt.yscale('log')
+ plt.grid('both')
+ plt.show()
+
+
+
--
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